1. Field of the Invention
The present invention relates to a object detection apparatus and control method thereof.
2. Description of the Related Art
As a technique for detecting an object from an image captured by a camera, a background subtraction method is known. In the background subtraction method, a fixed camera captures, in advance, an image of a background from which an object to be detected is removed, and stores feature amounts extracted from that image as a background model. After that, differences between feature amounts extracted from an image input from the camera and those in the background model are calculated, and a different region is detected as a foreground (object).
In this case, for example, an object such as a chair in a waiting room will be examined. The chair originally exists in the waiting room, and is not an object to be detected such as a person or a bag brought in by a person. However, people frequently moves the chair or changes its direction. If such change takes place, differences from the background model are generated, and the background subtraction method erroneously detects such change as an object.
In the present specification, an object such as a chair which originally exists in a background will be referred to as a background object hereinafter.
Hence, in Japanese Patent Laid-Open No. 2003-346156 (to be referred to as a literature hereinafter), after a change region from the background model is detected, the following processing is executed to distinguish the background object or a new object brought in a visual field, thereby preventing any detection errors. Feature amounts (color features and edge features) of a region corresponding to the change region of an input image are compared with those of a region corresponding to the change region of a background image generated from the background model, and if these feature amounts are similar to each other, it is determined that the background object is moved.
However, the technique according to the above literature erroneously detects a case in which new features which are not included in the background model appear upon movement or change of the background object. That is, since features of an input image are no longer similar to those included in the background image generated from the background model, a change of the background object is not determined. For example, when a red vase is placed in front of a blue wall, and a chair is placed in front of the red vase, features of the red vase are not included in the background model since the red vase is occluded behind the chair. When the chair is moved at this time, the occluded red vase appears in a video. In case of a chair, a backrest of which is rotated, when the backrest is rotated (out-of plane rotation), new features of the chair itself, which are not included in the background model, appear in a video. In this manner, new features which do not exist so far normally appear upon movement or rotation of the background object. That is, the above literature cannot sufficiently suppress any detection errors caused by a change of the background object.